Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Updata:01-01-1970

Source:
By:Qiaoyu Wang, Kai Kang, Zhihan Zhang, Demou Cao

DOI: https://doi.org/10.30564/aia.v3i1.2790

Abstract:

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory.

References:

[1] L. V. Litvintseva, S. V. Ulyanov, V. S. Ulyanov, Design of robust knowledge bases of fuzzy controllers for intelligent control of substantially nonlinear dynamic systems: II. A soft computing optimizer and robustness of intelligent control systems. J. of Computer and Systems Sci. Intern. 2006. (45) 5. 744-771 DOI:https://doi.org/10.1134/S106423070605008X. [2] L. V. Litvintseva, S. V. Ulyanov, I.S. Ulyanov, V. S. Ulyanov, Quantum fuzzy inference for knowledge base design in robust intelligent controllers. J. of Computer and Systems Sci. Intern. 2007. (46) 6. 908-961 DOI:https://doi.org/10.1134/S1064230707060081. [3] L. V. Litvintseva, S. V. Ulyanov, Intelligent control systems. I. Quantum computing and self-organization algorithm. J. of Computer and Systems Sci. Intern. 2009. (48) 6. 946-984 DOI:https://doi.org/10.1134/S1064230709060112. [4] L. V. Litvintseva, S. V. Ulyanov, Intelligent control systems. II. Design of self-organized robust knowledge bases in contingency control situations. J. of Computer and Systems Sci. Intern. 2011. (50) 2. 250-292 DOI:https://doi.org/10.1134/S1064230710061036. [5] S.V. Ulyanov, Quantum fuzzy inference based on quantum genetic algorithm: Quantum simulator in intelligent robotics. R. A. Aliev et al. (Eds.): ICSCCW 2019, AISC (1095). 1–8, 2020. DOI:https://doi.org/10.1007/978-3-030-35249-3_9.

Tags: